Field of the Invention
The present invention relates to a recording medium, a luminance computation apparatus, and a luminance computation method, and particularly to a technique for reducing a computation amount for a luminance computation.
Description of the Related Art
In recent years, in the field of rendering of graphics, application of global illumination to perform a more realistic graphics expression has been conducted. Because computation for global illumination performs computation that considers light paths from various lights for one point, a computation amount can increase in accordance with a reproduction precision. Because time constraints are involved in the field of real-time rendering in particular, various methods to reduce a computation amount have been proposed for global illumination.
As one method, there is a method that uses a Reflective Shadow Map (RSM) that includes, for each point of an object illuminated by a light, determined positions and parameters of a Bidirectional Reflectance Distribution Function (BRDF). With the method that uses the RSM, each pixel of the RSM indicates various parameters of indirect illumination (Virtual Point Light (VPL)) caused by lights defined in a scene, and by referencing the pixels, it is possible to calculate a contribution from each VPL to a radiance of a point that is the target of the computation.
However, because considering all pixels of the RSM as VPLs (summing contributions of all VPLs in a computation of radiance) means performing a computation even for a VPL having a low level of contribution, usually a scheme in which a random sampling is performed to obtain an approximate solution is employed. Specifically, for a point x on an object surface which is the target of computation, because a computation amount can become large even if the rendering equation for obtaining the radiance L(x,ω) for a direction ω to a viewpoint,L(x,ω)=∫ΩLin(x,ω′)f(x,ω,ω′)(ω′·n)dω′
(where Lin(x,ω′) is incident radiance, and f and n are the BRDF and normal of the object surface)
is limited to a resolution level for the RSM, a Monte Carlo integral that performs weight averaging by a random sampling of VPLs is employed.
Roman Prutkin, et al., “Reflective shadow map clustering for real-time global illumination”, Eurographics 2012 Short Papers, pp. 9-12 discloses a method that defines an importance map that expresses a two-dimensional probability density function (PDF) based on an RSM (a probability density is something that normalizes importance), uses k-means clustering to classify the map into a predetermined number of clusters (a higher importance is more finely classified), and approximates VPLs of each region corresponding to a cluster to one virtual light for which it is possible to analytically compute a radiance, to thereby reduce a resolution of the RSM, and reduce a computation amount for radiance.
However, because the k-means clustering recited in Prutkin performs processing to calculate a distance to each cluster for each pixel, a computation amount is typically large. Processing that uses k-means clustering to cluster pixels included in an image typically uses only a distance between pixels as a scale for the distance between a pixel and a cluster, and it is possible to classify pixels in the image into the shape of a Voronoi diagram, which as in FIG. 4 has no overlapping or gaps. In Prutkin, optimization of computation is performed by introducing different scales for such k-means clustering, but it was necessary to have a computation amount in accordance with a number of clusters for the classifying and the number of pixels of the importance map, and this was a form for which acceleration of the computation was difficult.